133 citations · 165 across the 13 of their papers we have counts for
13 papers
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…
From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to…
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection
Huaxin Zhang, Xiang Wang, Xiaohao Xu +6
In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite signif…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Sim…
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang +4
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspect…